Plotly

Dataset

library(tidyverse)

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## v tibble  3.1.4     v dplyr   1.0.7
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   2.0.1     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(p8105.datasets)

library(plotly)

## 
## 载入程辑包：'plotly'

## The following object is masked from 'package:ggplot2':
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## The following object is masked from 'package:stats':
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make the plot

This plot aims to show the total number of ordered items in each aisle.

data(instacart)

instacart_aisle = 
  instacart %>%
  group_by(aisle) %>%
  summarize(total = n()) %>%
  arrange(desc(total)) %>%
  filter(total > 10000) %>%
  mutate(aisle = fct_reorder(aisle, total)) %>%
  plot_ly(x = ~aisle, y = ~total, colors = ~aisle, type = "bar", alpha = 0.5)

ggplotly(instacart_aisle)

This plot aims to show the boxplots of the order hour of day of Pink Lady Apples and Coffee Ice Cream respectively.

instacart_hour = 
  instacart %>%
  filter(product_name %in% c("Pink Lady Apples", "Coffee Ice Cream")) %>%
  group_by(product_name, order_dow) %>%
  plot_ly(y = ~order_hour_of_day, color = ~product_name, type = "box", colors = "viridis")

ggplotly(instacart_hour)

This histogram aims to show the relationship between order hour and the day of Pink Lady Apples and Coffee Ice Cream respectively.

instacart_day = 
  instacart %>%
  filter(product_name %in% c("Pink Lady Apples", "Coffee Ice Cream")) %>%
  group_by(product_name, order_dow) %>%
  plot_ly(x = ~order_hour_of_day, y = ~order_dow, color = ~product_name, type = "histogram")

ggplotly(instacart_day)

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels